Storm Blueprints: Patterns for Distributed Real-time Computation

The core data structure in Storm is the tuple. A tuple is simply a list of named values (key-value pairs), and a Stream is an unbounded sequence of tuples. If you are familiar with complex event processing (CEP), you can think of Storm tuples as events.

Spouts represent the main entry point of data into a Storm topology. Spouts act as adapters that connect to a source of data, transform the data into tuples, and emit the tuples as a stream.

As you will see, Storm provides a simple API for implementing spouts. Developing a spout is largely a matter of writing the code necessary to consume data from a raw source or API. Potential data sources include:

Since spouts typically don't implement any specific business logic, they can often be reused across multiple topologies.

Bolts can be thought of as the operators or functions of your computation. They take as input any number of streams, process the data, and optionally emit one or more streams. Bolts may subscribe to streams emitted by spouts or other bolts, making it possible to create a complex network of stream transformations.

Bolts can perform any sort of processing imaginable and like the Spout API, the bolt interface is simple and straightforward. Typical functions performed by bolts include:

The SentenceSpout class will simply emit a stream of single-value tuples with the key name "sentence" and a string value (a sentence), as shown in the following code:

{ "sentence":"my dog has fleas" }

To keep things simple, the source of our data will be a static list of sentences that we loop over, emitting a tuple for every sentence. In a real-world application, a spout would typically connect to a dynamic source, such as tweets retrieved from the Twitter API.

{ "word" : "my" }
{ "word" : "dog" }
{ "word" : "has" }
{ "word" : "fleas" }

{ "word" : "dog", "count" : 5 }

Creating a new Storm project is just a matter of adding the Storm library and its dependencies to the Java classpath. However, as you'll learn in Chapter 2, Configuring Storm Clusters, deploying a Storm topology to a clustered environment requires special packaging of your compiled classes and dependencies. For this reason, it is highly recommended that you use a build management tool such as Apache Maven, Gradle, or Leinengen. For the distributed word count example, we will use Maven.

Let's begin by creating a new Maven project:

$ mvn archetype:create -DgroupId=storm.blueprints 
-DartifactId=Chapter1 -DpackageName=storm.blueprints.chapter1.v1

Next, edit the pom.xml file and add the Storm dependency:

<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-core</artifactId>
    <version>0.9.1-incubating</version>
</dependency>

Then, test the Maven configuration by building the project with the following command:

$ mvn install

Maven will download the Storm library and all its dependencies. With the project set up, we're now ready to begin writing our Storm application.

To keep things simple, our SentenceSpout implementation will simulate a data source by creating a static list of sentences that gets iterated. Each sentence is emitted as a single field tuple. The complete spout implementation is listed in Example 1.1.

Example 1.1: SentenceSpout.java

public class SentenceSpout extends BaseRichSpout {

    private SpoutOutputCollector collector;
    private String[] sentences = {
        "my dog has fleas",
        "i like cold beverages",
        "the dog ate my homework",
        "don't have a cow man",
        "i don't think i like fleas"
    };
    private int index = 0;

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("sentence"));
    }

    public void open(Map config, TopologyContext context, 
            SpoutOutputCollector collector) {
        this.collector = collector;
    }

    public void nextTuple() {
        this.collector.emit(new Values(sentences[index]));
        index++;
        if (index >= sentences.length) {
            index = 0;
        }
        Utils.waitForMillis(1);
    }
}

The BaseRichSpout class is a convenient implementation of the ISpout and IComponent interfaces and provides default implementations for methods we don't need in this example. Using this class allows us to focus only on the methods we need.

The declareOutputFields() method is defined in the IComponent interface that all Storm components (spouts and bolts) must implement and is used to tell Storm what streams a component will emit and the fields each stream's tuples will contain. In this case, we're declaring that our spout will emit a single (default) stream of tuples containing a single field ("sentence").

The open() method is defined in the ISpout interface and is called whenever a spout component is initialized. The open() method takes three parameters: a map containing the Storm configuration, a TopologyContext object that provides information about a components placed in a topology, and a SpoutOutputCollector object that provides methods for emitting tuples. In this example, we don't need to perform much in terms of initialization, so the open() implementation simply stores a reference to the SpoutOutputCollector object in an instance variable.

The nextTuple() method represents the core of any spout implementation. Storm calls this method to request that the spout emit tuples to the output collector. Here, we just emit the sentence at the current index, and increment the index.

The SplitSentenceBolt implementation is listed in Example 1.2.

Example 1.2 – SplitSentenceBolt.java

public class SplitSentenceBolt extends BaseRichBolt{
    private OutputCollector collector;

    public void prepare(Map config, TopologyContext context,
 OutputCollector collector) {
        this.collector = collector;
    }

    public void execute(Tuple tuple) {
        String sentence = tuple.getStringByField("sentence");
        String[] words = sentence.split(" ");
        for(String word : words){
            this.collector.emit(new Values(word));
        }
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word"));
    }
}

The BaseRichBolt class is another convenience class that implements both the IComponent and IBolt interfaces. Extending this class frees us from having to implement methods we're not concerned with and lets us focus on the functionality we need.

The prepare() method defined by the IBolt interface is analogous to the open() method of ISpout. This is where you would prepare resources such as database connections during bolt initialization. Like the SentenceSpout class, the SplitSentenceBolt class does not require much in terms of initialization, so the prepare() method simply saves a reference to the OutputCollector object.

In the declareOutputFields() method, the SplitSentenceBolt class declares a single stream of tuples, each containing one field ("word").

The core functionality of the SplitSentenceBolt class is contained in the execute() method defined by IBolt. This method is called every time the bolt receives a tuple from a stream to which it subscribes. In this case, it looks up the value of the "sentence" field of the incoming tuple as a string, splits the value into individual words, and emits a new tuple for each word.

The WordCountBolt class (Example 1.3) is the topology component that actually maintains the word count. In the bolt's prepare() method, we instantiate an instance of HashMap<String, Long> that will store all the words and their corresponding counts. It is common practice to instantiate most instance variables in the prepare() method. The reason behind this pattern lies in the fact that when a topology is deployed, its component spouts and bolts are serialized and sent across the network. If a spout or bolt has any non-serializable instance variables instantiated before serialization (created in the constructor, for example) a NotSerializableException will be thrown and the topology will fail to deploy. In this case, since HashMap<String, Long> is serializable, we could have safely instantiated it in the constructor. However, in general, it is best to limit constructor arguments to primitives and serializable objects and instantiate non-serializable objects in the prepare() method.

In the declareOutputFields() method, the WordCountBolt class declares a stream of tuples that will contain both the word received and the corresponding count. In the execute() method, we look up the count for the word received (initializing it to 0 if necessary), increment and store the count, and then emit a new tuple consisting of the word and current count. Emitting the count as a stream allows other bolts in the topology to subscribe to the stream and perform additional processing.

Example 1.3 – WordCountBolt.java

public class WordCountBolt extends BaseRichBolt{
    private OutputCollector collector;
    private HashMap<String, Long> counts = null;

    public void prepare(Map config, TopologyContext context, 
            OutputCollector collector) {
        this.collector = collector;
        this.counts = new HashMap<String, Long>();
    }

    public void execute(Tuple tuple) {
        String word = tuple.getStringByField("word");
        Long count = this.counts.get(word);
        if(count == null){
            count = 0L;
        }
        count++;
        this.counts.put(word, count);
        this.collector.emit(new Values(word, count));
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word", "count"));
    }
}

The purpose of the ReportBolt class is to produce a report of the counts for each word. Like the WordCountBolt class, it uses a HashMap<String, Long> object to record the counts, but in this case, it just stores the count received from the counter bolt.

One difference between the report bolt and the other bolts we've written so far is that it is a terminal bolt—it only receives tuples. Because it does not emit any streams, the declareOutputFields() method is left empty.

The report bolt also introduces the cleanup() method defined in the IBolt interface. Storm calls this method when a bolt is about to be shutdown. We exploit the cleanup() method here as a convenient way to output our final counts when the topology shuts down, but typically, the cleanup() method is used to release resources used by a bolt, such as open files or database connections.

One important thing to keep in mind about the IBolt.cleanup() method when writing bolts is that there is no guarantee that Storm will call it when a topology is running on a cluster. We'll discuss the reasons behind this when we talk about Storm's fault tolerance mechanisms in the next chapter. But for this example, we'll be running Storm in a development mode where the cleanup() method is guaranteed to be called.

The full source for the ReportBolt class is listed in Example 1.4.

Example 1.4 – ReportBolt.java

public class ReportBolt extends BaseRichBolt {

    private HashMap<String, Long> counts = null;

    public void prepare(Map config, TopologyContext context, OutputCollector collector) {
        this.counts = new HashMap<String, Long>();
    }

    public void execute(Tuple tuple) {
        String word = tuple.getStringByField("word");
        Long count = tuple.getLongByField("count");
        this.counts.put(word, count);
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        // this bolt does not emit anything
    }

    public void cleanup() {
        System.out.println("--- FINAL COUNTS ---");
        List<String> keys = new ArrayList<String>();
        keys.addAll(this.counts.keySet());
        Collections.sort(keys);
        for (String key : keys) {
            System.out.println(key + " : " + this.counts.get(key));
        }
        System.out.println("--------------");
    }
}

Now that we've defined the spout and bolts that will make up our computation, we're ready to wire them together into a runnable topology (refer to Example 1.5).

Example 1.5 – WordCountTopology.java

public class WordCountTopology {

    private static final String SENTENCE_SPOUT_ID = "sentence-spout";
    private static final String SPLIT_BOLT_ID = "split-bolt";
    private static final String COUNT_BOLT_ID = "count-bolt";
    private static final String REPORT_BOLT_ID = "report-bolt";
    private static final String TOPOLOGY_NAME = "word-count-topology";

    public static void main(String[] args) throws Exception {

        SentenceSpout spout = new SentenceSpout();
        SplitSentenceBolt splitBolt = new SplitSentenceBolt();
        WordCountBolt countBolt = new WordCountBolt();
        ReportBolt reportBolt = new ReportBolt();


        TopologyBuilder builder = new TopologyBuilder();

        builder.setSpout(SENTENCE_SPOUT_ID, spout);
        // SentenceSpout --> SplitSentenceBolt
        builder.setBolt(SPLIT_BOLT_ID, splitBolt)
                .shuffleGrouping(SENTENCE_SPOUT_ID);
        // SplitSentenceBolt --> WordCountBolt
        builder.setBolt(COUNT_BOLT_ID, countBolt)
                .fieldsGrouping(SPLIT_BOLT_ID, new Fields("word"));
        // WordCountBolt --> ReportBolt
        builder.setBolt(REPORT_BOLT_ID, reportBolt)
                .globalGrouping(COUNT_BOLT_ID);

        Config config = new Config();

        LocalCluster cluster = new LocalCluster();

        cluster.submitTopology(TOPOLOGY_NAME, config, builder.createTopology());
        waitForSeconds(10);
        cluster.killTopology(TOPOLOGY_NAME);
        cluster.shutdown();
    }
}

Storm topologies are typically defined and run (or submitted if the topology is being deployed to a cluster) in a Java main() method. In this example, we begin by defining string constants that will serve as unique identifiers for our Storm components. We begin the main() method by instantiating our spout and bolts and creating an instance of TopologyBuilder. The TopologyBuilder class provides a fluent-style API for defining the data flow between components in a topology. We start by registering the sentence spout and assigning it a unique ID:

builder.setSpout(SENTENCE_SPOUT_ID, spout);

The next step is to register SplitSentenceBolt and establish a subscription to the stream emitted by the SentenceSpout class:

builder.setBolt(SPLIT_BOLT_ID, splitBolt)
                .shuffleGrouping(SENTENCE_SPOUT_ID);

The setBolt() method registers a bolt with the TopologyBuilder class and returns an instance of BoltDeclarer that exposes methods for defining the input source(s) for a bolt. Here we pass in the unique ID we defined for the SentenceSpout object to the shuffleGrouping() method establishing the relationship. The shuffleGrouping() method tells Storm to shuffle tuples emitted by the SentenceSpout class and distribute them evenly among instances of the SplitSentenceBolt object. We will explain stream groupings in detail shortly in our discussion of parallelism in Storm.

The next line establishes the connection between the SplitSentenceBolt class and the WordCountBolt class:

builder.setBolt(COUNT_BOLT_ID, countBolt)
                .fieldsGrouping(SPLIT_BOLT_ID, new Fields("word"));

As you'll learn, there are times when it's imperative that tuples containing certain data get routed to a specific instance of a bolt. Here, we use the fieldsGrouping() method of the BoltDeclarer class to ensure that all tuples containing the same "word" value get routed to the same WordCountBolt instance.

The last step in defining our data flow is to route the stream of tuples emitted by the WordCountBolt instance to the ReportBolt class. In this case, we want all tuples emitted by WordCountBolt routed to a single ReportBolt task. This behavior is provided by the globalGrouping() method, as follows:

builder.setBolt(REPORT_BOLT_ID, reportBolt)
                .globalGrouping(COUNT_BOLT_ID);

With our data flow defined, the final step in running our word count computation is to build the topology and submit it to a cluster:

Config config = new Config();

LocalCluster cluster = new LocalCluster();

        cluster.submitTopology(TOPOLOGY_NAME, config, builder.createTopology());
        waitForSeconds(10);
        cluster.killTopology(TOPOLOGY_NAME);
        cluster.shutdown();

Here, we're running Storm in local mode using Storm's LocalCluster class to simulate a full-blown Storm cluster within our local development environment. Local mode is a convenient way to develop and test Storm applications without the overhead of deploying to a distributed cluster. Local mode also allows you to run Storm topologies within an IDE, setting breakpoints, halting execution, inspecting variables and profiling the application in ways that are much more time consuming or near impossible when deploying to a Storm cluster.

In this example, we create a LocalCluster instance and call the submitTopology() method with the topology name, an instance of backtype.storm.Config, and the Topology object returned by the TopologyBuilder class' createTopology() method. As you'll see in the next chapter, the submitTopology() method used to deploy a topology in local mode has the same signature as the method to deploy a topology in remote (distributed) mode.

Storm's Config class is simply an extension of HashMap<String, Object>, which defines a number of Storm-specific constants and convenience methods for configuring a topology's runtime behavior. When a topology is submitted, Storm will merge its predefined default configuration values with the contents of the Config instance passed to the submitTopology() method, and the result will be passed to the open() and prepare() methods of the topology spouts and bolts respectively. In this sense, the Config object represents a set of configuration parameters that are global to all components in a topology.

We're now ready to run the WordCountTopology class. The main() method will submit the topology, wait for ten seconds while it runs, kill (undeploy) the topology, and finally shut down the local cluster. When the program run is complete, you should see console output similar to the following:

--- FINAL COUNTS ---
a : 1426
ate : 1426
beverages : 1426
cold : 1426
cow : 1426
dog : 2852
don't : 2851
fleas : 2851
has : 1426
have : 1426
homework : 1426
i : 4276
like : 2851
man : 1426
my : 2852
the : 1426
think : 1425
-------------- 

So far in our word count example, we have not explicitly used any of Storm's parallelism APIs; instead, we allowed Storm to use its default settings. In most cases, unless overridden, Storm will default most parallelism settings to a factor of one.

Before changing the parallelism settings for our topology, let's consider how our topology will execute with the default settings. Assuming we have one machine (node), have assigned one worker to the topology, and allowed Storm to one task per executor, our topology execution would look like the following:

Topology execution

As you can see, the only parallelism we have is at the thread level. Each task runs on a separate thread within a single JVM. How can we increase the parallelism to more effectively utilize the hardware we have at our disposal? Let's start by increasing the number of workers and executors assigned to run our topology.

    Config config = new Config();
    config.setNumWorkers(2);

Configuring executors and tasks

As we've seen, Storm creates a single task for each component defined in a topology, by default, and assigns a single executor for each task. Storm's parallelism API offers control over this behavior by allowing you to set the number of executors per task as well as the number of tasks per executor.

The number of executors assigned to a given component is configured by setting a parallelism hint when defining a stream grouping. To illustrate this feature, let's modify our topology definition to parallelize SentenceSpout such that it is assigned two tasks and each task is assigned its own executor thread:

builder.setSpout(SENTENCE_SPOUT_ID, spout, 2);

If we're using one worker, the execution of our topology now looks like the following:

Two spout tasks

Next, we will set up the split sentence bolt to execute as four tasks with two executors. Each executor thread will be assigned two tasks to execute (4 / 2 = 2). We'll also configure the word count bolt to run as four tasks, each with its own executor thread:

builder.setBolt(SPLIT_BOLT_ID, splitBolt, 2)
              .setNumTasks(4)
                .shuffleGrouping(SENTENCE_SPOUT_ID);

builder.setBolt(COUNT_BOLT_ID, countBolt, 4)
                .fieldsGrouping(SPLIT_BOLT_ID, new Fields("word"));

With two workers, the execution of the topology will now look like the following diagram:

Parallelism with multiple workers

With the topology parallelism increased, running the updated WordCountTopology class should yield higher total counts for each word:

--- FINAL COUNTS ---
a : 2726
ate : 2722
beverages : 2723
cold : 2723
cow : 2726
dog : 5445
don't : 5444
fleas : 5451
has : 2723
have : 2722
homework : 2722
i : 8175
like : 5449
man : 2722
my : 5445
the : 2727
think : 2722
--------------

Since spout emits data indefinitely and only stops when the topology is killed, the actual counts will vary depending on the speed of your computer and what other processes are running on it, but you should see an overall increase in the number of words emitted and processed.

It's important to point out that increasing the number of workers has no effect when running a topology in local mode. A topology running in local mode always runs in a single JVM process, so only task and executor parallelism settings have any effect. Storm's local mode offers a decent approximation of cluster behavior and is very useful for development, but you should always test your application in a true clustered environment before moving to production.

In Storm, guaranteed message processing begins with the spout. A spout that supports guaranteed processing needs a way to keep track of tuples it has emitted and be prepared to re-emit a tuple if downstream processing of that tuple, or any child tuples, fails. A child tuple can be thought of as any tuple emitted as a result of a tuple originating from a spout. Another way to look at it is to consider the spout's stream(s) as the trunk of a tuple tree (shown in the following diagram):

Tuple tree

In the preceding diagram, the solid lines represent the original trunk tuples emitted by a spout, and the dotted lines represent tuples derived from the original tuple. The resulting graph represents the tuple tree. With guaranteed processing, each bolt in the tree can either acknowledge (ack) or fail a tuple. If all bolts in the tree acknowledge tuples derived from the trunk tuple, the spout's ack method will be called to indicate that message processing is complete. If any of the bolts in the tree explicitly fail a tuple, or if processing of the tuple tree exceeds the time-out period, the spout's fail method will be called.

Storm's ISpout interface defines three methods involved in the reliability API: nextTuple, ack, and fail.

public interface ISpout extends Serializable {
    void open(Map conf, TopologyContext context, SpoutOutputCollector collector);
    void close();
    void nextTuple();
    void ack(Object msgId);
    void fail(Object msgId);
}

As we've seen before, when Storm requests that a spout emit a tuple, it calls the nextTuple() method. The first step in implementing guaranteed processing is to assign the outbound tuple a unique ID and pass that value to the emit() method of SpoutOutputCollector:

collector.emit(new Values("value1", "value2") , msgId);

Assigning the tuple a message ID tells Storm that a spout would like to receive notifications either when the tuple tree is completed or if it fails at any point. If processing succeeds, the spout's ack() method will be called with the message ID assigned to the tuple. If processing fails or times out, the spout's fail method will be called.

Implementing a bolt that participates in guaranteed processing involves two steps:

Anchoring to a tuple means that we are creating a link between an incoming tuple and derived tuples such that any downstream bolts are expected to participate in the tuple tree by acknowledging the tuple, failing the tuple, or allowing it to time out.

You can anchor to a tuple (or a list of tuples) by calling one of the overloaded emit methods of OutputCollector:

collector.emit(tuple, new Values(word));

Here, we're anchoring to the incoming tuple and emitting a new tuple that downstream bolts should acknowledge or fail. An alternative form of the emit method will emit unanchored tuples:

collector.emit(new Values(word));));

Unanchored tuples do not participate in the reliability of a stream. If an unanchored tuple fails downstream, it will not cause a replay of the original root tuple.

After successfully processing a tuple and optionally emitting new or derived tuples, a bolt processing a reliable stream should acknowledge the inbound tuple:

this.collector.ack(tuple);

If tuple processing fails in such a way that the spout must replay (re-emit) the tuple, the bolt should explicitly fail the tuple:

this.collector.fail(tuple)

If tuple processing fails as a result of a time out or through an explicit call, the OutputCollector.fail() method, the spout that emitted the original tuple, will be notified, allowing it to re-emit the tuple, as you'll see shortly.

To further illustrate reliability, let's begin by enhancing the SentenceSpout class to make it support guaranteed delivery. It will need to keep track of all tuples emitted and assign each one a unique ID. We'll use a HashMap<UUID, Values> object to store the tuples that are pending. For each tuple we emit, we'll assign a unique identifier and store it in our map of pending tuples. When we receive an acknowledgement, we'll remove the tuple from our pending list. On failure, we'll replay the tuple:

public class SentenceSpout extends BaseRichSpout {
    
    
    private ConcurrentHashMap<UUID, Values> pending;
    private SpoutOutputCollector collector;
    private String[] sentences = {
        "my dog has fleas",
        "i like cold beverages",
        "the dog ate my homework",
        "don't have a cow man",
        "i don't think i like fleas"
    };
    private int index = 0;

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("sentence"));
    }

    public void open(Map config, TopologyContext context, 
            SpoutOutputCollector collector) {
        this.collector = collector;
        this.pending = new ConcurrentHashMap<UUID, Values>();
    }

    public void nextTuple() {
        Values values = new Values(sentences[index]);
        UUID msgId = UUID.randomUUID();
        this.pending.put(msgId, values);
        this.collector.emit(values, msgId);
        index++;
        if (index >= sentences.length) {
            index = 0;
        }
        Utils.waitForMillis(1);
    }

    public void ack(Object msgId) {
        this.pending.remove(msgId);
    }

    public void fail(Object msgId) {
        this.collector.emit(this.pending.get(msgId), msgId);
    }    
}

Modifying the bolts to provide guaranteed processing simply involves anchoring outbound tuples to the incoming tuple and then acknowledging the inbound tuple:

public class SplitSentenceBolt extends BaseRichBolt{
    private OutputCollector collector;

    public void prepare(Map config, TopologyContext context, OutputCollector collector) {
        this.collector = collector;
    }

    public void execute(Tuple tuple) {
        String sentence = tuple.getStringByField("sentence");
        String[] words = sentence.split(" ");
        for(String word : words){
            this.collector.emit(tuple, new Values(word));
        }
        this.collector.ack(tuple);
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("word"));
    }
}